About me: I've dabbled in a lot of fields for my age, both scientific and artistic. I'm a Doctor of Astronomy (nobody spends their time naming stars anymore; it's all applied physics and coding) who specialized in high-mass star formation and astrochemistry, with primary school vocational training in visual art, printmaking, and graphic design. I got my Bachelors of Science (Physics and Space Sciences double major) at Florida Tech, and earned my MS and PhD at University of Florida right before moving to Copenhagen for my pandemic-era postdoc. Now I'm at Lund University's LUNARC HPC center as an application expert, to focus more on coding and helping other researchers. As for what I enjoy working on, I'm a generalist who likes interdisciplinary projects, activities with educational other humanitarian value, and anything that makes me better at world building.

• 👾 What drew me to the position at LUNARC was the opportunity to have sneak peeks and maybe even interact with cutting-edge research in lots of different fields. I'm just getting started, and I'm mostly self-taught, but I hope this profile will start having more polished packages soon.
• 🔭 My last research position was a postdoc at the Niels Bohr Institute. My specialty was observational (MIR to submm) studies of the earliest stages of high-mass star formation, but over the course of the postdoc I built up my computational astrophysics portfolio with physical and astrochemical simulations. We were working on a project to do with what's called the "missing sulfur problem", which is actually a bundle of related problems, many of which are starting to be addressed by former colleagues who are now at Leiden working on JWST data. Sulfur has a relatively well-known abundance in the diffuse gas between stars and star-forming regions, but when gas cools and condenses just before forming a protostar, up to 99.9% of the S disappears from the gas phase. Once there's a hot protostar present, you get back about 10% of the S that went in, and my project showed that it's difficult to even explain that fraction with just thermal physics.
• 🌱 I recently took a course in Machine Learning and Data Science covering all of the following: Pandas, Seaborn, Plotly, Cufflinks, Chloropleth plots, Linear and Logistic Regression, K-Means, KNN, Decision Trees and Random Forests, Principal Component Analysis, Natural Language Processing, Neural Networks, and PySpark with AWS. I don't think I'm at liberty to post any of the project materials here, but I'm working on some demo projects I can post with public data sets from, e.g., Kaggle. I had prior experience with pandas and seaborn, so I'll focus more on the ML stuff.
• ✒️ Hobbies and Interests: writing, drawing, worldbuilding, D&D, boardgames with friends, sightseeing, beadwork, and hiking

About this account: A lot of the code generated for my research ties into other software that is not mine to disseminate. Aside from any Jupyter notebooks, the vast majority of my code files herein are ad hoc files intended for one or more highly specialized research tasks, and the need or opportunity to make them more generally usable never arose (although several were headed that direction at some point). The Jupyter notebooks are tutorials used for sessions where I presented at the Code and Coffee weekly meeting I used to run. Those ought to be more generally applicable.